| Zhou Li,Yanqi Feng,Piao Li,Shennan Wang,Ruichao Li,Shu Xia. Development and validation of a tumor microenvironment-related prognostic signature in lung adenocarcinoma and immune infiltration analysis. Oncol Transl Med, 2021, 7: 253-268. |
| 肺腺癌微环境相关预后模型的构建和验证及免疫浸润分析 |
| Development and validation of a tumor microenvironment-related prognostic signature in lung adenocarcinoma and immune infiltration analysis |
| Received:December 19, 2021 Revised:January 11, 2022 |
| DOI：10.1007/s10330-021-0545-5 |
| 中文关键词: 肺腺癌，肿瘤微环境，免疫治疗，免疫检查点分子，预后生物标志物 |
| 英文关键词: lung adenocarcinoma; tumor microenvironment; immunotherapy; immune checkpoint molecules; prognostic biomarkers |
| 基金项目:国家自然科学基金（No.81772471 、82172716） |
| Author Name | Affiliation | E-mail | | Zhou Li | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | lizhou166@163.com | | Yanqi Feng | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | | | Piao Li | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | | | Shennan Wang | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | | | Ruichao Li | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | | | Shu Xia* | Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology | xiashutj@hotmail.com |
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| 中文摘要: |
| 目的:肿瘤微环境（Tumor microenvironment，TME）中的肿瘤浸润免疫细胞和基质细胞显著影响着肺腺癌(lung adenocarcinoma, LUAD)的预后和免疫应答。在本研究中，我们旨在构建一个基于肺腺癌免疫和基质基因的TME相关预后模型。
方法:使用TCGA数据库中的LUAD样本作为训练队列构建模型，在3个GEO数据集中进行验证。采用ESTIMATE算法分析参与TME的免疫基因和基质基因。采用Kaplan-Meier和Cox回归分析筛选预后基因，并构建TME相关预后模型。通过GSEA和TIMER分析模型的免疫特征和相关信号通路。
结果:本研究建立了一个基于6个核心基因的TME相关预后模型，该模型可根据总生存率（OS）将患者分层为高风险组和低风险组。该模型对训练数据集(TCGA)和测试数据集(GEO)均具有较强的预测能力，并可作为LUAD的独立后因素。此外，低危组的免疫细胞浸润水平和抗肿瘤免疫活性均高于高危组。重要的是，该模型与免疫检查点分子密切相关，可作为预测患者对免疫治疗反应的指标。最后，核心基因BTK、CD28、INHA、PIK3CG、TLR4和VEGFD被可作为LUAD新的预后生物标志物，并与免疫细胞显著相关。
结论:本研究构建的TME预后模型能有效预测LUAD患者的预后，并能反映患者的TME状态。这6个核心基因为开发新的治疗策略提供了新的思路。 |
| 英文摘要: |
| Objective Tumor-infiltrating immune cells and stromal cells in the tumor microenvironment (TME)
significantly affect the prognosis of and immune response to lung adenocarcinoma (LUAD). In this study,
we aimed to develop a novel TME-related prognostic model based on immune and stromal genes in LUAD.
Methods LUAD data from the TCGA database were used as the training cohort, and three Gene
Expression Omnibus (GEO) datasets were used as the testing cohort. The Estimation of STromal and
Immune cells in MAlignant Tumor tissues using Expression data algorithm was used to analyze the immune
and stromal genes involved in the TME. Kaplan-Meier and Cox regression analyses were used to identify
prognostic genes and construct a TME-related prognostic model. Gene set enrichment analysis and TIMER
were used to analyze the immune features and signaling pathways of the model.
Results A TME-related prognostic model based on six hub genes was generated that significantly
stratified patients into the high- and low-risk groups in terms of overall survival. The model had strong
predictive ability in both the training (TCGA) and testing (GEO) datasets and could serve as an independent
prognostic factor for LUAD. Moreover, the low-risk group was characterized by greater immune cell
infiltration and antitumor immune activity than the high-risk group. Importantly, the signature was closely
associated with immune checkpoint molecules, which may serve as a predictor of patient response to
immunotherapy. Finally, the hub genes BTK, CD28, INHA, PIK3CG, TLR4, and VEGFD were considered
novel prognostic biomarkers for LUAD and were significantly correlated with immune cells.
Conclusion The TME-related prognostic model could effectively predict the prognosis and reflect the
TME status of LUAD. These six hub genes provided novel insights into the development of new therapeutic
strategies. |
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